RAG-CSI：用检索增强 Transformer 做 CSI 信道预测
RAG-CSI: Global Local Retrieval via Joint State and Dynamics Matching for CSI Prediction
一篇把 RAG 思路搬到无线信道预测的论文，检索历史信道知识来辅助预测，NMSE 降了 15.42%，做通信的可以看看。
论文提出 RAG-CSI，一个检索增强的 Transformer 框架，用于从历史观测预测未来的信道状态信息（CSI）。它构建了包含跨场景代表性观测和当前场景历史观测的全局-局部 CSI 知识库，并设计了联合状态-动力学检索（JSDR），同时刻画信道状态、相位动力学和振幅动力学来检索相关 CSI 序列。Transformer 预测器通过交叉注意力将检索到的知识与当前 CSI 历史融合进行预测。在多场景 CSI 数据集上，五项评估指标全部超过各自最优基线 10% 以上，其中归一化均方误差降低 15.42%。
RAG-CSI: Global Local Retrieval via Joint State and Dynamics Matching for CSI Prediction
Channel state information (CSI) prediction forecasts future channel states from historical observations, supporting transceiver design and resource allocation with reduced measurement overhead. However, existing deep learning predictors mainly encode channel knowledge in model parameters, which can limit their ability to represent diverse patterns of channel dynamics across scenes. In this paper, we propose RAG-CSI, a retrieval-enhanced Transformer framework that augments prediction with relevant historical channel knowledge. Specifically, we construct a global--local CSI knowledge store containing representative observations across scenes and historical observations from the current serving scene. We further develop joint state--dynamics retrieval (JSDR), which jointly characterizes channel states, phase dynamics, and amplitude dynamics to retrieve relevant CSI sequences. A Transformer predictor then uses cross-attention to fuse the retrieved knowledge with the current CSI history and predict future CSI with limited additional model parameters. Experiments on a multi-scene CSI dataset show overall improvements exceeding 10\% across all five evaluation metrics over the best baseline for each metric, including a 15.42\% reduction in normalized mean squared error.
